Evidence map›Paper›PMID 33320822›Full record

ArticleJMIR formative research2021

A Novel Artificial Intelligence-Powered Emotional Intelligence and Mindfulness App (Ajivar) for the College Student Population During the COVID-19 Pandemic: Quantitative Questionnaire Study.

Ronda Sturgill, Mary Martinasek, Trine Schmidt, Raj Goyal

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
1.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.

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  11. Perspectives on Artificial Intelligence in Nursing in Asia.Asian/Pacific Island nursing journal · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Ronda Sturgill *Department of Health Sciences and Human Performance, The University of Tampa, Tampa, FL, United States.ORCID https://orcid.org/0000-0003-3530-8497
Mary Martinasek *Department of Health Sciences and Human Performance, The University of Tampa, Tampa, FL, United States.ORCID https://orcid.org/0000-0003-4502-5431
Trine Schmidt *Ajivar, Tarpon Springs, FL, United States.ORCID https://orcid.org/0000-0003-2082-2733
Raj Goyal *Ajivar, Tarpon Springs, FL, United States.ORCID https://orcid.org/0000-0002-4365-6304
Univar (United States) · USUniversity of Tampa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmotional intelligence (EI) and mindfulness can impact the level of anxiety and depression that an individual experiences. These symptoms have been exacerbated among college students during the COVID-19 pandemic. Ajivar is an app that utilizes artificial intelligence (AI) and machine learning to deliver personalized mindfulness and EI training.

objectiveThe main objective of this research study was to determine the effectiveness of delivering an EI curriculum and mindfulness techniques using an AI conversation platform, Ajivar, to improve symptoms of anxiety and depression during this pandemic.

methodsA total of 99 subjects, aged 18 to 29 years, were recruited from a second-semester group of freshmen students. All participants completed the online TestWell Wellness Inventory at the start and end of the 14-week semester. The comparison group members (49/99, 49%) were given routine mental wellness instruction. The intervention group members (50/99, 51%) were required to complete Ajivar activities in addition to routine mental wellness instruction during the semester, which coincided with the onset of the COVID-19 pandemic. This group also completed assessments to evaluate for anxiety, using the 7-item Generalized Anxiety Disorder (GAD-7) scale, and depression, using the 9-item Patient Health Questionnaire (PHQ-9).

resultsStudy participants reported a mean age of 19.9 (SD 1.94) years; 27% (27/99) of the group were male and 60% (59/99) identified as Caucasian. No significant demographic differences existed between the comparison and intervention groups. Subjects in the intervention group interacted with Ajivar for a mean time of 1424 (SD 1168) minutes. There was a significant decrease in anxiety, as measured by the GAD-7: the mean score was 11.47 (SD 1.85) at the start of the study compared to 6.27 (SD 1.44) at the end (P<.001). There was a significant reduction in the symptoms of depression measured by the PHQ-9: the mean score was 10.69 (SD 2.04) at the start of the study compared to 6.69 (SD 2.41) at the end (P=.001). Both the intervention and comparison groups independently had significant improvements in the TestWell Wellness Inventory from pretest to posttest. The subgroups in the social awareness and spirituality inventories showed significant improvement in the intervention group. In a subgroup of participants (11/49, 22%) where the GAD-7 was available during the onset of the COVID-19 pandemic, there was an increase in anxiety from the start of the study (mean score 11.63, SD 2.16) to mid-March (ie, onset of the pandemic) (mean score 13.03, SD 1.48; P=.23), followed by a significant decrease at the end of the study period (mean score 5.9, SD 1.44; P=.001).

conclusionsIt is possible to deliver EI and mindfulness training in a scalable way using the Ajivar app during the COVID-19 pandemic, resulting in improvements in anxiety, depression, and EI in the college student population.

Indexed as

college studentsCOVID-19emotional intelligencemindfulness

Identifiers

PMID33320822
PMCPMC7787688
OpenAlexW3111543327

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.